Applying Deep Learning for Predicting Retention in PrEP Care and Effective PrEP Use among Key Populations at Risk for HIV in Thailand

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Project Summary/Abstract HIV remains a major cause of morbidity and mortality despite great progress in HIV prevention and treatment, especially for key populations (KPs), including men who have sex with men (MSM) and transgender women (TGW). Pre-exposure prophylaxis (PrEP) has been shown effective in reducing HIV acquisition among different populations when implemented as part of a combination prevention strategy. However, effectiveness of PrEP decreases with suboptimal retention and adherence. While many efforts have been made to assess adherence to PrEP and its associations with HIV prevention effectiveness, more research is needed to deepen our understanding of individual-level facilitators and barriers to retention in care and adherence to PrEP. Machine learning holds promise to address those effectively due to its ability to model complex non-linear relationships among many interacting factors without relying on modeling assumptions, and recent advances in deep learning have resulted in exciting results for a variety of clinical prediction applications. Although machine learning has been applied to identify potential PrEP candidates, little is done in exploring machine learning, especially advanced deep learning techniques, to assess predictive factors for retention in PrEP care and effective PrEP use. To close gaps in knowledge, the proposed study aims to explore advanced machine learning techniques to identify protective and risk factors for retention in PrEP care and effective PrEP use among key populations in Thailand. We will perform descriptive statistical analysis to characterize PrEP use patterns among MSM and TGW (Aim 1); develop deep learning models to predict loss to follow up in PrEP care and effective PrEP use (Aim 2); and design an explainable risk scoring system for identifying clients at high risk of discontinuation and non-effective PrEP use, with interpretable reasoning logic and associated demographic, behavioral, social, and clinical factors (Aim 3). This study is responsive to NIMH’s priority research in HIV prevention and strategic goal 3.2 to develop strategies for tailoring existing interventions to optimize outcomes. The findings from this study and the prediction-model based scoring system will inform tailored interventions to optimize PrEP engagement and facilitate differentiated PrEP service delivery, paving a solid foundation for precise HIV prevention using PrEP as an effective strategy.